PowerHerd: a distributed scheme for dynamically satisfying peak-power constraints in interconnection networks
Why this work is in the frame
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Bibliographic record
Abstract
As interconnection networks proliferate to a wide range of high-performance systems, power consumption is becoming a significant architectural issue. In interconnection networks, the peak-power consumption directly affects the solution for package cooling and power-delivery design. Off-line worst-case power analysis is typically used to estimate the network peak-power consumption and guarantee safe online operation, which not only increases system cost, but also constrains network performance. In this paper, we present an online mechanism, called PowerHerd, to efficiently manage network power resources at runtime, and guarantee that network peak-power constraints are not exceeded. PowerHerd is a distributed approach-within the interconnection network, each router dynamically maintains a local power budget, controls its local power dissipation, and exchanges spare power resources with its neighboring routers to optimize network performance. Experiments demonstrate that PowerHerd can effectively regulate network power consumption, meeting peak-power constraints with negligible network-performance penalty. Armed with PowerHerd, network designers can focus on system performance and power optimization for the average case, rather than the worst-case, thus making it possible to employ a more powerful interconnection network in the system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it